Interactive charts

Plotly

Styling a Plotly chart

# Wrangle data for plotting
iris_bar_df <- iris %>% 
  group_by(Species) %>% 
  summarise(Sepal_Width = mean(Sepal.Width)) %>% 
  ungroup()

# Create basic bar chart (left)
plotly_iris_bar <- plot_ly(iris_bar_df,
                    x = ~Species,
                    y = ~Sepal_Width,
                    type = "bar")

# Create formatted chart (right)
plotly_iris_bar_formatted <- plot_ly(iris_bar_df,
                    x = ~Species,
                    y = ~Sepal_Width,
                    type = "bar",
                    marker = list(color = nice_colours[["bold_teal_100"]],
                                  line = list(color = nice_colours[['black_100']], width = 1.5)))

plotly_iris_bar

plotly_iris_bar_formatted

# fig.show = "hold" not working for Plotly?

plotly_iris_bar_themed <- plotly_iris_bar_formatted %>% 
  nice_plotly_theme(chart_type = "vertical_bar",
             x_title = "Species",
             y_title = "Sepal Width")

Formatted chart headline title

Figure x. Formatted chart statistical title

Alt text (text description of message chart is showing. Mark chart as decorative)
Source: []
Download the data for Figure x (CSV, 5.0KB).

How to save your plot

To download the plot as a static PNG image, click on the camera icon at the top right of the plot.

Saving in other file formats (e.g. .svg or .jpg) is more complicated and requires installation of another piece of software. Read the Plotly documentation on exporting graphs as static images in R.

Chart types

# Load in DOACs data
load(file = here::here("data", "doacs_df.rda"))

Histogram

Use chart_type = “horizontal_bar” for histograms.

# Create chart
plotly_hist <- iris %>%
  plot_ly(x = ~Sepal.Width,
          color = ~Species,
          # Use NICE colours - remove colour names so it maps correctly
          colors = unname(nice_palettes[["discrete"]][1:3]),
          type = "histogram",
          # Black outline for bars
          marker = list(line = list(color = nice_colours[['black_100']], width = 1.5)),
          # Set bin width
          xbins = list(size = 0.2,
                       start = 1.9,
                       end = 4.5)) %>% 
  # Stack bars
  layout(barmode = "stack") %>% 
  # Add NICE theme and set axis titles
  nice_plotly_theme(chart_type = "vertical_bar",
             x_title = "Sepal width",
             y_title = "Frequency")

plotly_hist

# Category order differs from ggplot but no big difference

Bar chart

Vertical bar chart

Use chart_type = “vertical_bar”.

# Prepare data, get total items dispensed for each medicine in 2021 (in millions)
bar_df <- doacs_df %>% 
  filter(between(date, as.Date("2021-01-01"), as.Date("2021-12-31"))) %>% 
  group_by(chemical) %>% 
  summarise(items = sum(items)/1000000) %>% 
  ungroup()


# Create chart
plotly_vbar_chart <- bar_df %>%
  # Reorder chemical factor levels in decreasing order of items
  mutate(chemical = reorder(chemical, -items)) %>% 
  plot_ly(x = ~chemical,
          y = ~items,
          type = "bar",
          # Bold teal bars with black outline
          marker = list(color = nice_colours[["bold_teal_100"]],
                        line = list(color = nice_colours[['black_100']], width = 1.5))) %>% 
  # Add NICE theme and set axis titles
  nice_plotly_theme(chart_type = "vertical_bar",
             x_title = "",
             y_title = "Dispensed items (millions)")

Apixaban was the most prescribed DOAC in 2021

Figure x. Total DOAC medicines dispensed in primary care in England, 2020

Horizontal bar chart

Use chart_type = “horizontal_bar”.

# Create chart
plotly_vbar_chart <- bar_df %>%
  # Reorder chemical factor levels in decreasing order of items
  mutate(chemical = reorder(chemical, items)) %>% 
  plot_ly(x = ~items,
          y = ~chemical,
          type = "bar",
          # Make horizontal
          orientation = "h",
          # Bold teal bars with black outline
          marker = list(color = nice_colours[["bold_teal_100"]],
                        line = list(color = nice_colours[['black_100']], width = 1.5))) %>% 
  # Add NICE theme and set axis titles
  nice_plotly_theme(chart_type = "horizontal_bar",
             x_title = "Dispensed items (millions)",
             y_title = "")

plotly_vbar_chart

Line chart

Use chart_type = “line”.

# Prepare data
line_df <- doacs_df %>%
  filter(chemical == "Edoxaban") %>% 
  group_by(date, chemical) %>%
  summarise(items = sum(items)) %>%
  ungroup()

# Create chart
plotly_line_chart <- line_df %>%
  plot_ly(x = ~date,
          y = ~items,
          type = "scatter",
          # Line with marker dot at each data point
          mode = "lines+markers",
          # Make line and dots teal
          marker = list(color = nice_colours[["bold_teal_100"]])) %>%
  # Add the vertical dashed line to show lockdown date. Added this before plotting the 
  # data so that it sits on a layer behind the line and points
  add_lines(x = as.Date("2020-03-01"),
            y = ~c(0, max(items)),
            line = list(dash = "dash",
                        color = "#000000",
                        width = 1.5),
            # Don't inherit properties of previous trace
            inherit = FALSE) %>% 
  layout(showlegend = FALSE,
         # Y axis ticks with commas as thousands separators
         xaxis = list(type = "date", # Specify x axis is date
                      # Show x axis ticks
                      ticks = "outside", 
                      # Format ticks as abbreviated month name and full year, e.g. Jan 2018
                      tickformat = "%b\n%Y",
                      # Set first tick
                      tick0 = "2017-07-01",
                      # Tick every 6 months
                      dtick = "M6"),
         yaxis = list(tickformat = ",",
                      range = ~c(0, max(items)+10000)),
         annotations = list(x = as.Date("2020-03-01"),
                            xshift = -60,
                            y = 180000,
                            text = "National lockdown\non 23 March",
                            xref = "x",
                            yref = "y",
                            showarrow = FALSE, 
                            align = "right")) %>% 
  # Add NICE theme and set axis titles
  nice_plotly_theme(chart_type = "line",
             x_title = "",
             y_title = "Dispensed items",
             # Don't pad the axes, as ticks shown. Do not want gap between axis line and ticks
             pad_axes = FALSE)

Edoxaban prescribing has increased since 2017
Figure x. Edoxaban prescribing in England, 2017-2022

Own tests

economics_long %>%
  filter(variable != "pop") %>%
  plot_ly(x = ~date,
         y = ~value01,
         color = ~variable,
         colors = c("#00436C", "#D07B4D", "#000000", "#37906D"),
         type = "scatter",
         mode = "lines") %>%
  nice_plotly_theme(chart_type = "line")

plot1 <- mpg %>%
  filter(manufacturer %in% c("hyundai", "nissan", "toyota")) %>%
  count(manufacturer) %>%
  plot_ly(y = ~manufacturer,
          x = ~n,
          color = ~manufacturer,
          type = "bar",
          orientation = "h",
          marker = list(line = list(color = '#000000', width = 1.5)))

nice_plotly_theme(plot1, chart_type = "horizontal_bar")

mpg %>%
  filter(manufacturer %in% c("hyundai", "nissan", "toyota")) %>%
  count(manufacturer) %>%
  plot_ly(y = ~manufacturer,
          x = ~n,
          color = ~manufacturer,
          type = "bar",
          orientation = "h",
          marker = list(line = list(color = '#000000', width = 1.5))) %>%
  nice_plotly_theme(chart_type = "horizontal_bar")

mpg %>%
  filter(manufacturer %in% c("hyundai", "nissan", "toyota")) %>%
  count(manufacturer) %>%
  plot_ly(x = ~manufacturer,
          y = ~n,
          color = ~manufacturer,
          type = "bar",
          marker = list(line = list(color = '#000000', width = 1.5))) %>%
  nice_plotly_theme(chart_type = "vertical_bar")

Test plotly themes

Impact team

2022-12-22